Instructions to use togethercomputer/evo-1-8k-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use togethercomputer/evo-1-8k-base with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-generation", model="togethercomputer/evo-1-8k-base", trust_remote_code=True)

# Load model directly
from transformers import AutoModelForCausalLM
model = AutoModelForCausalLM.from_pretrained("togethercomputer/evo-1-8k-base", trust_remote_code=True, dtype="auto")

Notebooks
Google Colab
Kaggle
Local Apps

vLLM

How to use togethercomputer/evo-1-8k-base with vLLM:

Install from pip and serve model

# Install vLLM from pip:
pip install vllm
# Start the vLLM server:
vllm serve "togethercomputer/evo-1-8k-base"
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:8000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "togethercomputer/evo-1-8k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker

docker model run hf.co/togethercomputer/evo-1-8k-base

SGLang

How to use togethercomputer/evo-1-8k-base with SGLang:

Install from pip and serve model

# Install SGLang from pip:
pip install sglang
# Start the SGLang server:
python3 -m sglang.launch_server \
    --model-path "togethercomputer/evo-1-8k-base" \
    --host 0.0.0.0 \
    --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "togethercomputer/evo-1-8k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Use Docker images

docker run --gpus all \
    --shm-size 32g \
    -p 30000:30000 \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    --env "HF_TOKEN=<secret>" \
    --ipc=host \
    lmsysorg/sglang:latest \
    python3 -m sglang.launch_server \
        --model-path "togethercomputer/evo-1-8k-base" \
        --host 0.0.0.0 \
        --port 30000
# Call the server using curl (OpenAI-compatible API):
curl -X POST "http://localhost:30000/v1/completions" \
	-H "Content-Type: application/json" \
	--data '{
		"model": "togethercomputer/evo-1-8k-base",
		"prompt": "Once upon a time,",
		"max_tokens": 512,
		"temperature": 0.5
	}'

Docker Model Runner
How to use togethercomputer/evo-1-8k-base with Docker Model Runner:
```
docker model run hf.co/togethercomputer/evo-1-8k-base
```

evo-1-8k-base / config.json

Zymrael

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	{
	"_commit_hash": "1cc23830f62c268082475776fb449af8428eb703",
	"_name_or_path": "togethercomputer/evo-1-131k-base",
	"architectures": [
	"StripedHyenaModelForCausalLM"
	],
	"attn_layer_idxs": [
	8,
	16,
	24
	],
	"auto_map": {
	"AutoConfig": "togethercomputer/evo-1-131k-base--configuration_hyena.StripedHyenaConfig",
	"AutoModelForCausalLM": "togethercomputer/evo-1-131k-base--modeling_hyena.StripedHyenaModelForCausalLM",
	"AutoTokenizer": [
	"togethercomputer/evo-1-131k-base--tokenizer.ByteTokenizer",
	null
	]
	},
	"column_split": false,
	"column_split_hyena": true,
	"eps": 1e-06,
	"final_norm": true,
	"hidden_size": 4096,
	"hyena_filter_groups": 1,
	"hyena_layer_idxs": [
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	],
	"inference_mode": false,
	"inner_mlp_size": 10928,
	"log_intermediate_values": false,
	"make_vocab_size_divisible_by": 8,
	"max_seqlen": 8192,
	"mha_out_proj_bias": true,
	"mlp_activation": "gelu",
	"model_parallel_size": 1,
	"model_type": "stripedhyena",
	"num_attention_heads": 32,
	"num_filters": 4096,
	"num_layers": 32,
	"pipe_parallel_size": 1,
	"prefill_style": "fft",
	"proj_groups": 1,
	"qkv_proj_bias": true,
	"rotary_emb_base": 10000,
	"rotary_emb_scaling_factor": 1,
	"short_filter_bias": true,
	"short_filter_length": 3,
	"smeared_gqa": false,
	"split_k0": true,
	"state_size": 8,
	"tie_embeddings": true,
	"torch_dtype": "bfloat16",
	"transformers_version": null,
	"use_cache": true,
	"use_flash_attn": true,
	"use_flash_depthwise": false,
	"use_flash_rmsnorm": false,
	"use_flashfft": false,
	"use_interpolated_rotary_pos_emb": false,
	"vocab_size": 512
	}